Pith. sign in

Paper Citation Record · LEDGER

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response

As of 7 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.26791.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26791 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T21:00:41.702871Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65a73d8f-47a2-4b3d-8c94-ec1fd1f7d23d · outbound

This paper cites International Conference on Learning Representations , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response International Conference on Learning Representations , volume=

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.517408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.517408Z digest=sha256:d34c3c6970a0db8e641c63c8364d6c6ae2a00d23cbe9fc8aede5e45c337f1e7d

Observation 6fa4a1dd-b030-4e1b-b55f-c967358fcae6 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Advances in Neural Information Processing Systems , volume=

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.524581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.524581Z digest=sha256:7c2534dd45df5edc89597241852df8b534ecc4bbdd6ae3219b75f64e5aa484ff

Observation da23d235-37b0-4ae6-87df-18657f435f7c · outbound

This paper cites CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.529969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.529969Z digest=sha256:98f394942e3864c2cc468678dba06d8cf11018d9394076b99f1be7ce71067ff0

Observation 6ee0aea9-d838-4ddd-b8c3-5384590d2656 · outbound

This paper cites SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.535613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.535613Z digest=sha256:16f4aeeee10f4782912cd7a1e622354c8db0e90c587a0d7b85c675dd973a2302

Observation e146040f-e1d8-4838-a174-a653bd2748ee · outbound

This paper cites CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.541387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.541387Z digest=sha256:c756d6e880d33464c6311d9dbf98031e08cb0ffab99bd540258dbec66ef9c296

Observation 8b24fd9c-745f-4d6f-9375-d06b2fce7900 · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.547042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.547042Z digest=sha256:2d03f0b2d959f8749d9cd66c7a780b14556f3bbfa66ec79788a4776770162b5b

Observation 5b6d748a-f0b8-4398-bbb7-d928f68111f0 · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.552564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.552564Z digest=sha256:92b47d859050621bfa0886f59b6b790fd972a4e9d0e9039749da57777ae6bea9

Observation 0945a461-a7fd-4a47-b11d-1ccc49dab343 · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.557176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.557176Z digest=sha256:93a2eba417b9d751edbb8ca555464512ca5cac92c21c93f4d6f00e3408ef4707

Observation adacc18a-a90b-4dd8-b719-6e17cb89ac75 · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.562081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.562081Z digest=sha256:b1ed5f97ff3c1d00baae0ba3b893aaa3f8e903332232fae1b7abd2fa0fef9135

Observation 179bf145-0fb0-44f9-b7e8-f4ed5bd37b0a · outbound

This paper cites arXiv e-prints , pages=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response arXiv e-prints , pages=

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.567183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.567183Z digest=sha256:59df5ec8dafaab9c1bca6ae334015f89cb15249f80f97257c7b498af685387ef

Observation 188e63fb-ff05-42db-9132-8b718e63eb15 · outbound

This paper cites arXiv preprint arXiv:2509.20166 , year=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response arXiv preprint arXiv:2509.20166 , year=

Reference 11

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.572553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.572553Z digest=sha256:eb55efc01e6135616c38f4785eb2703406a2882b32dd11da75a0375bae7d0fa3

Observation 339d76bc-4a63-407b-b885-fe3d97c7034b · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.577757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.577757Z digest=sha256:5b4e84326279a7b02c51b1323aa8c7c56805e860889e577109d20ad8f2d4667a

Observation d526a461-1dbe-400a-a5db-bbee2f526969 · outbound

This paper cites The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.582935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.582935Z digest=sha256:60a744a9a1ed39dac93df15a2e664354748e6e8f4365656c5504c84c1b440d59

Observation 7ac2c3c1-3c08-42f6-a60b-ce6610518fff · outbound

This paper cites 2026 , url =.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response 2026 , url =

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.588208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.588208Z digest=sha256:5cb1e650cbab313c8715527f47a9da26f9cd7f068bd06923707a8c6f678cc4f2

Observation 0e566c2f-6ce1-4b2b-b0a4-33b4463ad4fa · outbound

This paper cites 2026 , url =.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response 2026 , url =

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.593443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.593443Z digest=sha256:5bb7a83d850b6a471c4d15d6260c1f9e7187ddc95d03ef6a19468bc8159f5f43

Observation a6782eaa-c6eb-494c-b9b3-501d0e107908 · outbound

This paper cites 2026 , url =.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response 2026 , url =

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.598466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.598466Z digest=sha256:21d47cbc3ba2132369cca1951e2ea55d210137884a2d17d1938868bf19730ac1

Observation 2ab9afa4-5a21-45cf-8c49-f10c2eeaa132 · outbound

This paper cites 2026 , url =.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response 2026 , url =

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.603452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.603452Z digest=sha256:2ec8f431f1c7eefa29bb2ea9d3c4c52e40686587e9bca2dd5f09e5b15baf8b62

Observation 362a57c9-8f14-4320-a338-6daa55557c30 · outbound

This paper cites Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.608843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.608843Z digest=sha256:b18c20d1f612383351e279c5cd4a9251af1d7a02c6edbafa231d4a5e8f480783

Observation 7c60f8ae-408b-45cd-93b6-8a12928b763e · outbound

This paper cites Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.614003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.614003Z digest=sha256:6c2fb165dfa4a7d7123543f63c93d7e31c90fdf1f6ed4fd438d2e43663e9317b

Observation cc70400c-c646-441c-9b44-e74e046dc4b3 · outbound

This paper cites SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.619162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.619162Z digest=sha256:b71748a79394585219ea10943171471c8242cad6fe9a8466c06fc797db5c9b82

Observation dc6bb50b-2a19-469c-a450-b3b36f8fce88 · outbound

This paper cites ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation

Reference 21

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.624509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.624509Z digest=sha256:39c6cce5b232cd06449497922162ab34f24efde59d1a9a287c5335e3b0405c83

Observation 20617a1d-0429-41de-8cba-8c669a116e33 · outbound

This paper cites arXiv preprint arXiv:2505.20945 , year=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response arXiv preprint arXiv:2505.20945 , year=

Reference 22

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.629658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.629658Z digest=sha256:345fd7079d24e98a34ddfacf5057085380c13ac457cc029e266c40fe8d178847

Observation 7afb3e77-30cd-4b5a-8fae-2b4c980dbb6c · outbound

This paper cites 2026 , url =.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response 2026 , url =

Reference 23

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.634384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.634384Z digest=sha256:65f0fdf763e781249eea7eab6c56595baef2f0b5e5666b5ed35f63509af481e8

Observation b5e637c8-b398-4f9a-8bc2-3a9188b7e020 · outbound

This paper cites International Conference on Learning Representations , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response International Conference on Learning Representations , volume=

Reference 24

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.639946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.639946Z digest=sha256:8b07443defd1439f0f9be6353a4039eea2e9b3aea02680078c5f7629bc6df217

Observation 563c8513-5ea0-48bd-827d-cb04a890b093 · outbound

This paper cites SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.644675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.644675Z digest=sha256:d498dc0cc96f9e5aac570ffeb1ef98002d402f47567be229906a7b5215f94f29

Observation 6a75d7cc-e79e-441e-8b2d-0b661d5b73e5 · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 26

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.650026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.650026Z digest=sha256:288c93411800e1e02d537caa34846b0cf03117d5ea3e7bfd2340c9b66d300016

Observation 8ba23d9b-d41a-44ca-a988-a6f9c4bb03d0 · outbound

This paper cites arXiv preprint arXiv:2509.26490 , year=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response arXiv preprint arXiv:2509.26490 , year=

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.655370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.655370Z digest=sha256:069ea09d7a0ccce68e48c9d3e40b3b701d6fcc5555ea58ef410413cb7fdeaefb

Observation 6f3db46e-8b06-4f74-9a93-f45e1ed3e4bf · outbound

This paper cites an unresolved cited work.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Unresolved cited work

Reference 28

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.660282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.660282Z digest=sha256:300a7001d43061ab60772239e66bb9a12d189ab6c1e108e4822c723d6e5ea871

Observation 6f225847-4c60-4c32-97c7-fcaa74f6b088 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Advances in Neural Information Processing Systems , volume=

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.666835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.666835Z digest=sha256:7d734ebfdfd40054709dc801cd352db4b89a0d4aae49bb57b32026c7cd247331

Observation a638a963-19d7-4d92-b6ed-3801a9ce8efe · outbound

This paper cites International Conference on Learning Representations , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response International Conference on Learning Representations , volume=

Reference 30

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.673056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.673056Z digest=sha256:ff8069b314effc465306f4533414a48d1bd8e9b6f1b9e6a6931fed6d4cf8f2e8

Observation e826a8a5-c19c-4252-9a4b-9885966751c3 · outbound

This paper cites ClawBench: Can AI Agents Complete Everyday Online Tasks?.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response ClawBench: Can AI Agents Complete Everyday Online Tasks?

Reference 31

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.678312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.678312Z digest=sha256:cbbf3af4b18989cf58fdf973492036df895514ca72f66f8fa14c62f5c9d40e09

Observation 499a03c3-3259-435b-aaae-9575b1c4e0bd · outbound

This paper cites CocoaBench: Evaluating Unified Digital Agents in the Wild.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response CocoaBench: Evaluating Unified Digital Agents in the Wild

Reference 32

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.683585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.683585Z digest=sha256:01537ca9bca0cb6411974486c8b363ee1730a71ba0729e485875c24ecf855187

Observation 5672d8bc-615b-429a-acef-9de952255485 · outbound

This paper cites Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows

Reference 33

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.689026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.689026Z digest=sha256:c2122da99c633e50357f63b3e6b2ce33c4e8645941bcb153e06c1cadb33eaf42

Observation b179562d-339b-4674-9811-043c814e470d · outbound

This paper cites Advances in neural information processing systems , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response Advances in neural information processing systems , volume=

Reference 34

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.697518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.697518Z digest=sha256:68d35d35cd240c887ca2df1dc2962cc7ff3b2fe917a3773b0b05f17bc472e8e3

Observation 7d047b56-ef33-44b5-8f70-b82404f510c7 · outbound

This paper cites The Innovation , volume=.

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response The Innovation , volume=

Reference 35

Resolution
unresolved
no resolver link, observed 2026-07-30T21:00:41.702871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T21:00:41.702871Z digest=sha256:7b0f5c3cd7a62412a63a6824b3810398ad76f9aa29bdd93e8a704bad27a37557

Pith citing papers

No inbound Pith citation observations are available.